Reinforcement Learning for Real - World Applications - A Comprehensive Review

摘要

Reinforcement learning (RL) has been transformed from theory to real -life application in the domains of healthcare fashion and many more.This decision paper highlights the history of RL in robotics, autonomous vehicles, business operations, business healthcare, and more.We kickstart RL in sequential decision -making where little human engineering is required. Major algorithms, including profound Q -network, actor -critic methods, and distributional RL, have been implemented into the processes of finding human -level policies to solve complex tasks. Simulation innovations, hierarchical RL, as well as a more efficient transfer from a simulator to the real world have increased the applicability. For instance, the robotics used in manufacturing processes, self -driving cars, supply chain management, automated trading, medical assistance systems, and gaming AI are all examples of their application. RL is responsible for full autonomy in making the best dynamic decisions, which, in turn, leads to increased automation and productivity. The long -term efforts of overcoming the bottlenecks of sample efficiency, safety, sim -to -real transfer, and stability are in the process of being actively investigated. With the development of parallelism and transfer learning, the effect of RL on real -world problems will be more striking.